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In This Issue
Engineering, Technology, and the Future of Work
Autonomous Vehicles Implications for Employment Demand
Creating New Value with Performance-Based Industrial Systems Design and Operations Management An Engineering Opportunity
Education Transformation to Support Lifelong Learning
Beyond Schooling Educating for the Unknowable Future
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Fall Bridge Issue on Engineering, Technology, and the Future of Work
September 15, 2015 Volume 45 Issue 3

Engineering, Technology, and the Future of Work

Tuesday, September 15, 2015

Author: Nicholas M. Donofrio and Katie S. Whitefoot

Editors’ Note

Anyone who caught an episode of Mad Men—or worked in an office in the 1970s—can easily see how technological developments have changed the workplace. Rows of typists transcribing documents from Dictaphones were replaced by personal computers and word processing software used directly by their would-be bosses. In factories, computer-aided design and manufacturing replaced the reams of paper used to encode machining instructions for each part via holes punched in the paper tape, along with the drafters and tape encoders who prepared the part drawings and machining instructions by hand. These developments enabled huge improvements in productivity and cost savings, and also transformed how work was done and what types of jobs were available.

The evolution of work, enabled by engineering advances, continues today. Developments in machine learning, computer vision, and compliant actuators, for example, allow robots to work alongside people in factories, hospitals, and retail stores; autonomous vehicles to travel across the country with little input from human drivers; and IBM’s Watson to predict the health outcomes of patients with chronic diseases. These advances will affect the ways that engineers, pharmacists, storekeepers, truckers, and many others approach their work in the coming years.

In Washington DC, responses to these technological developments and their potential influence on the future of work range between fascination and mild panic. In just the past few months, more than a dozen policy forums have raised the question of whether robots and other automated technologies will destroy jobs; one Washington Post columnist called it the “Great Robot Freakout of 2015” (Rampell 2015).

There are several good reasons to pay attention to the impact of technological innovations on the workplace and jobs. Employment growth rates in production, sales, and office administration occupations have generally been sinking since the 1980s, a decrease attributed to the falling costs of automating routine, codifiable tasks. Employment growth in the manufacturing sector has largely been driven by demand for workers with four-year or graduate degrees, while the long-term availability of jobs for less educated workers has declined. And these trends predate the recent recession, meaning that they are likely to continue even after the economy fully recovers.

Unfortunately, too often what is lost in the national conversation on technology and human capital is the recognition that we—engineers, policymakers, educators, and business leaders—have the ability to influence this dynamic from both sides. We have a role to play in creating a system where people can more easily gain the education and skills needed to work with advancing technologies, thus improving job prospects for a larger share of the population. And by encouraging the design of machines that not only are easy to use but actively help workers contribute additional value to the job—while at the same time automating tasks that are dangerous or boring—we support improved workplace conditions that better complement people’s skills.

The articles in this issue consider the many roles of engineers in shaping the future of work: through the technological systems they design, the business opportunities they identify, the students they educate, and the resources they create to facilitate learning throughout one’s career.

In the first article, Jennifer M. Miller examines the potential impacts of autonomous vehicles on employment and the nature of work in occupations that require driving. By analyzing detailed data on activities performed on the job, she categorizes occupations into those that may be open to more people as vehicles become more automated, those that may be eliminated, and those that can incorporate more value-added activities in place of driving (think school bus drivers who help students learn while in transit). These categories can help both employers and workers anticipate how job requirements are likely to change, and can help autonomous vehicle makers understand what types of features to design into the vehicles to accommodate the changing nature of work in different occupations.

Chris Johnson explains how changing business models in industrial infrastructure, such as power generation, transit, and manufacturing plants, are shifting the role of companies that provide these systems and the engineers that work for them. As industrial infrastructure providers offer more services to support engineering design, operations, and financing over the lifecycle of the systems they produce, these product-service systems require engineers who can model the performance of an infrastructure system over its life, diagnose the system’s condition based on observed data, and identify opportunities for solutions that improve the efficient use and value of the system over time.

Educating students for the changing nature of work is the focus of an article by Katharine G. Frase, who examines possible approaches for K–12 and college pedagogy as well as the use of data to enable personalized guidance for learners. She explains the need for partnerships between employers, educators, and government to craft curricular content that keeps pace with the evolving knowledge and capabilities needed for current and emerging careers.

John A. Alic examines workforce adaptability beyond formal education. He posits that informal learning—for example, through on-the-job experience and online educational content—is increasingly important as technological change reshapes the labor market. To help people gain additional skills and make career changes, he advocates for the creation of new institutional structures and tools to support “just in time” learning.

The last of our invited articles illustrates the role of public-private partnerships in helping both communities and companies prosper amid technological change. Dan Swinney describes three national and international examples of regional collaborations that promote workforce and economic development in the manufacturing sector. He makes the case that coalitions of small businesses, schools, research institutions, labor, and government are effective in spurring the growth of regional economic clusters that develop large numbers of both thriving small businesses and well-paid jobs.

The theme of this issue was inspired by the NAE’s Manufacturing, Design, and Innovation (MDI) program. We are proud to be part of a team that, as part of that program, was charged by the NAE to examine how technological advances, new business models, and intensifying globalization are transforming manufacturing and high-tech value chains. The results of the team’s first year and a half of work are presented in NAE’s report on Making Value for America: Embracing the Future of Manufacturing, Technology, and Work (NAE 2015; available at www.nap.edu). The NAE is launching a second phase, on “Educating the Engineering and Technical Workforce for Resiliency to Change,” that will examine educational and organizational approaches to improve the ability of the workforce to adapt to technological changes.1

We thank all the authors and staff who contributed to this special issue, especially Managing Editor Cameron Fletcher.

References

NAE [National Academy of Engineering]. 2015. Making Value for America: Embracing the Future of Manufacturing, Technology, and Work. Washington: National Academies Press.

Rampell C. 2015. The robots aren’t threatening your job. Washington Post, April 9.


FOOTNOTES

1 The NAE point of contact for the project is Amelia Greer (AGreer@nae.edu).

 

About the Author:Nicholas M. Donofrio (NAE) is IBM Fellow Emeritus and retired executive vice president for innovation and technology, IBM Corporation. Katie S. Whitefoot is an assistant professor of mechanical engineering and engineering and public policy at Carnegie Mellon University and former NAE senior program officer.